Data processor, data processing method and storage medium

ABSTRACT

According to one embodiment, a data processor includes an image acquisition module, a degradation evaluation module, a first output module and a display module. The first output module is configured to output a first trigger for performing a process for detecting the image area when the possibility is high as a result of evaluation by the degradation evaluation module, the first output module is configured to output a command for displaying the image as it is on a display when the possibility is low as a result of the evaluation.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority fromJapanese Patent Application No. 2015-045899, filed Mar. 9, 2015, theentire contents of which are incorporated herein by reference.

FIELD

Embodiments described herein relate generally to a data processor, adata processing method and a storage medium.

BACKGROUND

Recently, the following data processor has become widespread. The dataprocessor detects characters described in an advertising display, asign, a paper, etc., from an image captured by a camera and applies acharacter recognition process and a translation process to the detectedcharacters. When using the data processor, the user needs to recognizewhere is currently captured by the camera through the preview screendisplayed on the display, move the camera (data processor) toward thecharacters to be captured and set the characters so as to be within theimaging range. This process is called framing.

The framing process can be more easily performed as the time requireduntil preview display which displays the preview screen on, the displayis shorter (in other words, as the refresh rate of preview display ishigher). However, at the moment, every time an image is captured by thecamera, characters are detected from the image, and further, a characterrecognition process or a translation process is performed for thedetected characters. Thus, the refresh rate becomes low, therebycreating difficulty in the above framing process.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a function block diagram showing a configuration example of adata processor according to a first embodiment.

FIG. 2 is a pattern diagram shown for explaining a character candidatedetection process performed by a character candidate detector accordingto the first embodiment.

FIG. 3 is a pattern diagram shown for explaining first detection resultinformation obtained as a result of the character candidate detectionprocess performed by the character candidate detector according to thefirst embodiment.

FIG. 4 is a pattern diagram shown for explaining the principle ofstraight line Hough transform.

FIG. 5 is another pattern diagram shown for explaining the principle ofstraight line Hough transform.

FIG. 6 is a pattern diagram shown for explaining Hough voting.

FIG. 7 is a flowchart showing an example of the operation of the dataprocessor according to the first embodiment.

FIG. 8 is a pattern diagram shown for explaining differences between aconventional data processing method and a data processing method of thefirst embodiment.

FIG. 9 is a pattern diagram shown for explaining a framing period.

FIG. 10 is a pattern diagram shown for explaining various icons forsuggesting the framing state.

FIG. 11 is a pattern diagram shown for explaining an icon for suggestinga degradation evaluation value.

FIG. 12 is a function block diagram showing an example of the hardwareconfiguration of the data processor according to the first embodiment.

FIG. 13 is a function block diagram showing a configuration example of adata processor according to a second embodiment.

FIG. 14 is a pattern diagram shown for explaining the relationshipbetween the distribution of character candidates and a densityevaluation value.

FIG. 15 is a flowchart showing an example of the operation of the dataprocessor according to the second embodiment.

FIG. 16 is a pattern diagram shown for explaining differences between aconventional data processing method and a data processing method of thesecond embodiment.

FIG. 17 is a pattern diagram shown for explaining an icon for suggestingthe density evaluation value.

FIG. 18 is a flowchart showing an example of the operation of a dataprocessor according to a third embodiment.

FIG. 19 is a pattern diagram shown for explaining differences between aconventional data processing method and a data processing method of thethird embodiment.

DETAILED DESCRIPTION

Embodiments will be described hereinafter with reference to theaccompanying drawings.

In general, according to one embodiment, a data processor includes animage acquisition module, a degradation evaluation module, a firstoutput module and a display module. The image acquisition module isconfigured to acquire an image obtained by capturing a character surfaceon which a character row including a plurality of characters isdescribed. The degradation evaluation module is configured, to evaluatewhether a possibility that an image area which seems to correspond to acharacter is detected from the image is high based on a degradationevaluation value indicating a degree of degradation of the image. Thefirst output module is configured to output a first trigger forperforming a process for detecting the image area when the possibilityis high as a result of evaluation by the degradation evaluation module,the first output module is configured to output a command for displayingthe image as it is on a display when the possibility is low as a resultof the evaluation. The display module is configured to display, bydetecting a predetermined number of image areas or more which seem tocorrespond to characters from the image, an image prepared by processingthe image areas in accordance with the output first trigger, or displaythe image acquired by the image acquisition module as it is inaccordance with the output command.

First Embodiment

FIG. 1 is a function block diagram showing a configuration example of adata processor 10 according to a first embodiment. As shown in FIG. 1,the data processor 10 includes, for example, an, image acquisitionmodule 101, a degradation evaluation module 102, a character detectiondictionary storage 103, a character candidate detector 104, a characterrow detector 105, an application module 106 and an output module 107. Inthe present embodiment, the data processor 10 is assumed to be a tabletterminal.

The image acquisition module 101 obtains an image captured by using acamera function. In the present embodiment, characters described in anadvertising display, a sign, a paper, etc., are assumed to be captured.

The degradation evaluation module 102 obtains the pose change amount ofthe data processor 10 at the time of capturing the image obtained by theimage acquisition module 101 from an acceleration sensor, an angularvelocity sensor, etc., incorporated into the data processor 10. The posechange amount is a value indicating how fast the data processor 10 (inother words, an imaging units provided in the data processor 10, such asa camera) was moved when the image was captured. There is asubstantially positive correlation between the pose change amount andthe degree of blurring generated in the captured image. It is highlypossible that the captured image is much blurred when the pose changeamount is large. If the image is much blurred, there is a highpossibility that no character candidate can be detected in the charactercandidate detection process described later. Thus, the pose changeamount is an index for determining the probability of failure in thecharacter candidate detection process described later. When theacceleration sensor is used, the pose change amount may be the magnitudeof a velocity vector obtained through temporal integration of anacceleration vector from which a gravitation component is removed. Theblurring of an image is influenced by the rotational movement of thecamera more than the translational movement. Therefore, the magnitude ofthe rotational velocity obtained by the angular velocity sensor may beregarded as an approximate pose change amount, ignoring thetranslational movement. The response of these sensors is quick. Thus,the pose change amount can be calculated with limited computation. Inthe explanation below, the pose change amount may be referred to as adegradation evaluation value since the blurring indicates the degree ofdegradation of the captured image.

The degradation evaluation module 102 compares the obtained degradationevaluation value with a predetermined threshold. Only when thedegradation evaluation value is less than or equal to the threshold, thedegradation evaluation module 102 outputs a first trigger for performingthe character candidate detection process described later. When thedegradation evaluation value is greater than the predeterminedthreshold, the degradation evaluation module 102 outputs a command forperforming the preview display process explained later to the outputmodule 107.

In the above explanation, the pose change amount measured by a sensorunit such as the acceleration sensor is used as the degradationevaluation value. However, for example, the contrast value of an imageis small when the image is blurred (note that the contrast value can beobtained as the difference between the maximum brightness and theminimum brightness). Utilizing this feature, the degradation evaluationvalue may be obtained. Specifically, the contrast value of the imageobtained by the image acquisition module 101 is calculated. Further, thecontrast value is subtracted from a predetermined constant number. Thevalue obtained in this manner may be used as the degradation evaluationvalue. Apart from the above, as in optical flow, the magnitude of amotion vector in the image may be directly calculated, and for example,the maximum value of the calculated magnitude of the motion vectors inthe whole image may be used as the degradation evaluation value. Thus,the above processes can be performed even in a data processor which doesnot include a built-in acceleration sensor, etc., by directlycalculating the degradation evaluation value from the image obtained bythe image acquisition module 101.

In the above explanation, the first trigger is output when thedegradation evaluation value is less than or equal to the predeterminedthreshold. However, for example, even when, the degradation evaluationvalue is less than or equal to the threshold, the image is blurred ifthe camera is not focused on the target. This blurred image has anadverse impact on the character candidate detection process describedlayer. Thus, the first trigger may be output only when the degradationevaluation value is less than or equal, to a threshold, and further, thecamera is focused on the target.

The character detection dictionary storage 103 is a storage deviceconfigured to store a character detection dictionary used by thecharacter candidate detector 104.

When the input of the first trigger output by the degradation evaluationmodule 102 is received, the character candidate detector 104 performs acharacter candidate detection process for detecting an image area whichseems to include a character in the image obtained by the imageacquisition module 101 as a character candidate (in short, an area inwhich a character seems to be described).

Now, a character candidate detection process performed by, the charactercandidate detector 104 is explained in detail with reference to FIG. 2.

The character candidate detector 104 applies a contraction process tothe image (input image) obtained by the image acquisition module 101,generates a resolution pyramid image and performs a character candidatedetection process for searching for or detecting a character on theresolution pyramid image. Specifically, as shown in FIG. 2, thecharacter candidate detector 104 contracts an input image 201 obtainedby the image acquisition module 101 at a constant rate r (0<r<1) inseries and generates one or more re-size images 202 and 203. The numberof re-size images to be generated, in other words, the number ofcontraction, processes to be performed depends on the minimum size andthe maximum size of the character to be detected in the specification.The size of a detection window 205 in FIG. 2 is determined in accordancewith the size of the character to be detected on the input image 201having the highest resolution. Thus, the size of the detection window205 is the minimum size of the character to be detected in thespecification. On the contracted re-size images 202 and 203 obtained bymultiplying the constant rate r, the range covered by the detectionwindow 205 having the same size is wide. Accordingly, the character tobe detected becomes large. The character candidate detector 104generates a re-size image until, the size of the character exceeds themaximum size of the character to be detected in the specification. Afterone or more re-size images are generated in this manner, the charactercandidate detector 104 generates a resolution pyramid image 204 bycombining the input image 201 and the re-size images 202 and 203, asshown in FIG. 2.

After generating the resolution pyramid image 204, the charactercandidate detector 104 scans each of the images 201 to 203 included inthe generated resolution pyramid image 204 through the detection window205 having a predetermined size, cuts out the image included in thedetection window 205 in each position and generates a plurality ofpartial images. The character candidate detector 104 detects a charactercandidate based on the generated partial images and the characterdetection dictionary stored in, the character detection dictionarystorage 103. Specifically, the character candidate detector 104 checkseach, of the partial images and the character detection dictionary,calculates the score indicating character-likeness for each of thepartial images and determines whether or not each score exceeds apredetermined threshold. In this manner, it is possible to determine(evaluate) whether or not each of the partial images is an image whichincludes a character. In accordance with this determination result, thecharacter candidate detector 104 provides a partial image determined asan image which includes a character with a first code indicating thatthe image include a character, and provides a partial image determinedas an image which does not include a character (in other words, an imageincluding something which is not a character) with a second codeindicating that the image does not include a character. In this manner,the character candidate detector 104 is configured to detect an area inwhich a partial image having the first code is present (in other words,an area in which the detection window 205 from which a partial imagehaving the first code is cut out is located) as an area in which acharacter is present.

When the number of partial images having the first code is greater thanor equal to a predetermined threshold as a result of the charactercandidate detection process, the character candidate detector 104outputs first detection result information indicating an area in which acharacter is present on the input image 201 to the character rowdetector 105. For example, as shown in FIG. 3, an area in which acharacter is present on the input image 201 is shown by a rectangularframe in the first detection result information. When the number ofpartial images having the first code is less than the predeterminedthreshold, the character candidate detector 104 outputs a command forperforming the preview display process described later to the outputmodule 107.

A well-known pattern identification method such as a subspace method ora support vector machine can be used to realize the score calculationmethod for evaluating the character-likeliness of a partial imageincluded in the detection window 205. Therefore, the detailedexplanation of the score calculation method is omitted in the presentembodiment.

Now, this specification returns to the explanation of FIG. 1. Afterreceiving the input of the first detection result information output bythe character candidate detector 104, the character row detector 105performs a character row detection process for detecting a character rowdescribed in the image obtained by the image acquisition module 101based on the first detection result information. The character rowdetection process is a process for detecting the linear alignment ofcharacter candidates, using straight line Hough transform.

The principle of straight line Hough transform is explained firstly withreference to FIG. 4.

The explanation of the principle of straight line Hough transform isbegun with a discussion of a Hough curve. As exemplarily shown asstraight lines 301 to 303 in FIG. 4, the number of straight lines whichcan pass through a point p (x, y) on the two-dimensional coordinate iscountless. However, when the gradient of a perpendicular line 304extended from the original point O to each straight line from the x-axisis defined as θ, and the length, of the perpendicular line 304 isdefined as ρ, θ and ρ are uniquely determined for each straight line.The combination of θ and ρ uniquely determined for each of countlessstraight lines which can pass through a point (x, y) is known to draw aunique locus 305 (p=x cos θ+y sin θ) in accordance with the values of(x, y) on the θ-ρ coordinate system. In general, the locus 305 is calleda Hough curve.

Straight line Hough transform indicates that straight lines which canpass through coordinate values (x, y) are transformed to a Hough curvedrawn by θ, φ uniquely determined as explained above. When the straightline which passes through (x, y) inclines to left, θ is positive. Whenthe straight line is perpendicular, θ is zero. When the straight lineinclines to right, θ is negative. The domain of θ does not go beyond therange of −π<θ<−π.

A Hough curve can be obtained independently with respect to each pointin the x-y coordinate system. However, for example, as shown in FIG. 5,straight line 401 which passes through three points p1 to p3 in commoncan be obtained as a straight line determined by the coordinates (θo,ρo) of point 405 which is the intersection of Hough curves 402 to 404corresponding to points p1 to p3, respectively. The more points thestraight line passes through, the more Hough curves pass through theposition of θ and ρ indicating the straight line. Thus, straight lineHough transform is considered to be suitable for detecting a straightline from a group of points.

When a straight line is detected from a group of points, an engineeringmethod called Hough voting is employed. In this method, the combinationof θ and ρ of each Hough curve is voted in the two-dimensional Houghvoting space having the θ- and ρ-coordinate axes. This voting suggeststhe combination of θ and ρ through which a large number of Hough curvespass, in other words, the presence of a straight line which passesthrough a large number of points, in a position which gained a largenumber of votes in the Hough voting space. In general, a two dimensionalarray (Hough voting space) is prepared such that the array has the sizeof a necessary search range for θ and ρ. Further, the number of votesobtained is reset to zero. Subsequently, a Hough curve is obtained foreach point by the above Hough transform. Only the value of one is addedto, on the array, a position through which the Hough curve passes. Thismethod is called Hough voting in general. After the above Hough votingis performed for all of the points, the following facts are revealed. Nostraight line is present in a position which did not gain any vote (inother words, a position through which no Hough curve passes). A straightline which passes through only one point is present in a position whichgained one vote (in other words, a position through which only one Houghcurve passes). A straight line which passes through two points ispresent in a position which gained two votes (in other words, a positionthrough which two Hough curves pass). Further, a straight line whichpasses through n points is present in a position which gained n votes(in other words, a position through which n Hough curves pass). Inshort, a straight line which passes through two or more points on thex-y coordinate system appears as a position which gained two or morevotes in the Hough voting space.

If the resolution in the Hough voting space can be made infinite, asdescribed above, only a point on a locus gains votes whose number isequal to the number of loci which pass through the point. However, theactual Hough voting space is quantized with an appropriate resolutionwith respect to θ and ρ. Thus, in the distribution of the number ofvotes obtained, the circumference of the intersection position of aplurality of loci also shows a large number of votes obtained.Therefore, the intersection position of loci is obtained by looking forthe position having the local maximum value in the distribution of thenumber of votes obtained in the Hough, voting space.

With reference to FIG. 6, this specification explains a character rowdetection process using the above straight line Hough transform andHough, voting. Here, the plane of the input image is assumed to be theplane of coordinates 501 in which the horizontal axis is x and thevertical axis is y.

When the central coordinates on the image of a character candidate 502are (x, y), an infinite number of straight lines pass through thispoint. These straight lines certainly satisfy the above equation forstraight line Hough transform defined as: ρ=x cos θ+y sin θ. Asdescribed above, ρ and θ indicate the length of the perpendicular lineextended, from the original point O to each straight line on the x-ycoordinate system and the gradient of the perpendicular line from thex-axis, respectively. The values of (θ, ρ) satisfied by straight lineswhich pass through a point (x, y) form a Hough curve on the θ-ρcoordinate system. A straight line which passes through two differentpoints can be expressed by the combination of (θ, ρ) which is theintersection point of the Hough curves of the two points. The characterrow detector 105 obtains the Hough curves of character candidatesdetected by the character candidate detector 104 from the central pointsof the character candidates. When the character row detector 105discovers the combination of (θ, ρ) on which a large number of Houghcurves intersect, the character row detector 105 detects the presence ofa straight line in which a large number of character candidates arelinearly aligned; in short, the presence of a character row.

To discover the combination of (θ, ρ) on which a large number of Houghcurves intersect, the character row detector 105 votes a Hough curvecalculated from the central coordinates of a character candidate in theHough voting space. In the Hough voting space, as shown in FIG. 6, thevertical axis is ρ, and the horizontal axis is θ. Further, a pluralityof Hough voting spaces 503 to 505 are prepared in accordance with thesize s of the character candidate 502, as shown in FIG. 6. When, thecharacter candidate 502 is small, the character candidate 502 is votedin Hough voting space 503 for a small size s. When the charactercandidate 502 is large, the character candidate 502 is voted in a Houghvoting space 505 for a large size s. The character row detector 105detects the straight line defined by a local maximum position (θ, ρ)which gained votes more than or equal to a predetermined threshold ineach Hough voting space. The character row detector 105 detects theassembly of character candidates which voted the straight line as acharacter row. When the character row detector 105 detects a pluralityof straight lines defined by local maximum positions (θ, ρ) which gainedvotes more than or equal to a predetermined threshold on one Houghcurve, the character row detector 105 detects the assembly of charactercandidates which voted the straight line having the highest number ofvotes obtained as a character row. For example, when the predeterminedthreshold is two, a local maximum position 506 which gained three votesis detected as a character row by the character row detector 105 inHough voting space 503 in FIG. 6, beating out the local maximumpositions which gained two votes. In Hough voting space 505 in FIG. 6, alocal maximum position 507 which gained two votes is detected as onlyone character row by the character row detector 105. Thus, two straightlines corresponding to local maximum positions 506 and 507,respectively, are detected from the input image. After the straight lineare detected, the character row detector 105 extracts charactercandidates which voted each straight line and detects a character row asthe area covered by the candidates.

When local maximum positions detected in different Hough voting spacesadjacent to each other in the size s are close to each other within apredetermined distance, the character row detector 105 determines thatthe same character row is detected separately, and detects these localmaximum positions as one character row from the assembly of charactercandidates which voted the local maximum positions.

When one or more character rows are detected as a result of thecharacter row detection process, the character row detector 105 outputssecond detection result information indicating the area in which the oneor more character rows are present to the application module 106. Whenno character row is detected as a result of the character row detectionprocess, the character row detector 105 outputs a command for performingthe preview display process described later to the output module 107.

Now this specification returns to the explanation of FIG. 1. Theapplication module 106 performs a process unique to an applicationinstalled in advance, using the second detection result informationoutput by the character row detector 105. For example, when anapplication configured to perform a character recognition process (forexample, an application having an OCR function) is installed in advance,the application module 106 extracts the image pattern of the area inwhich a character row is present shown by the second detection resultinformation. The application module 106 applies a character recognitionprocess to the extracted image pattern of the character row and obtainsa character code string corresponding to the character row in the area.

When the characters in an image are recognized by an OCR function, etc.,the application module 106 is configured to search for informationrelated to the obtained character code string. Specifically, theapplication module 106 is configured to search for information such asthe price or specification from a product name, obtain, from the name ofa place or a noted place, map information to get to the place, andtranslate a language into another language. The process resultinformation showing the result of the process performed by theapplication module 106 is output to the output module 107.

The output module 107 performs a preview display process forsuperimposing the process result information output by the applicationmodule 106 on the image obtained by the image acquisition module 101 anddisplaying the image on the display of the data processor 10. When theoutput module 107 receives the input of a command, for performing apreview display process from each module different from the applicationmodule 106, the output module 107 performs a preview display process forat least displaying the input image on the display as it is inaccordance with the command.

Now, this specification explains an example of the operation of the dataprocessor 10 structured in the following manner with reference to theflowchart of FIG. 7.

First, the image acquisition module 101 obtains (acquires) an imagewhich is captured by a camera function (step S1). Subsequently, thedegradation evaluation module 102 obtains, from the acceleration sensor,etc., incorporated into the data processor 10, the degradationevaluation value at the time of capturing the image obtained by theimage acquisition module 101. The degradation evaluation module 102determines (evaluates) whether or not the obtained degradationevaluation value is less than or equal to a predetermined threshold(step S2). When the degradation evaluation value exceeds the thresholdas a result of the determination process in step S2 (NO in step S2), theprocess proceeds to step S8 for preview-displaying the obtained image asit is as described later.

When the degradation evaluation value is less than or equal to thethreshold as a result of the determination process in step S2 (YES instep S2), the degradation evaluation module 102 outputs the firsttrigger for performing a character candidate detection process to thecharacter candidate detector 104. After receiving the input of the firsttrigger output by the degradation evaluation module 102, the charactercandidate detector 104 applies a character candidate detection processto the image obtained by the image acquisition module 101 (step S3).

Subsequently, the character candidate detector 104 determines whether ornot a predetermined number of character candidates or more are detectedas a result of the character candidate detection process in step S3(step S4). When a predetermined number of character candidates or moreare not detected as a result of the determination process in step S4 (NOin step S4), the process proceeds to step S8 for preview-displaying theobtained image as it is as described later.

When a predetermined number of character candidates or more are detectedas a result of the determination process in step S4 (YES in step S4),the character row detector 105 applies a character row detection processto the image obtained by the image acquisition module 101 based on thefirst detection result information obtained as a result of the charactercandidate detection process in step S3 (step S5).

Subsequently, the character row detector 105 determines whether or notone or more character rows are detected as a result of the character rowdetection process in step S5 (step S6). When no character row isdetected as a result of the determination process in step S6 (NO in stepS6), the process proceeds to step S8 for preview-displaying the obtainedimage as it is as described later.

When one or more character rows are detected as a result of thedetermination process in step S6 (YES in step S6), the character rowdetector 105 outputs the second detection result information obtained asa result of the character row detection process in step S5 to theapplication module 106. The application module 106 performs a process(for example, a character recognition process or a translation process)unique to an application installed in advance based on, the seconddetection, result information output by the character row detector 105,and outputs the process result information showing the process result tothe output module 107 (step S7).

After receiving the input of the process result information output bythe application module 106, the output module 107 performs a previewdisplay process for superimposing the process result information on theimage obtained by the image acquisition module 101 and displaying theimage on, the display. When the input of a command for performing apreview display process is received from each module different from theapplication module 106, the output module 107 displays the imageobtained by the image acquisition module 101 on the display as it is(step S8) and terminates the process.

Now, this specification explains differences between the conventionaldata processing method and the data processing method of the presentembodiment with reference to FIG. 8. Mainly, this specification explainsdifferences between the methods in terms of a framing period and arefresh rate.

A framing period refers to a period from when the user starts moving thedata processor 10 toward the character string to be captured to when theuser obtains an image which leads to the acquisition of a desiredcharacter recognition, result or translation result for the framingthrough, for example, display output (in short, if the user processesthe image, a desired result will be obtained). A framing period can bedivided into three major stages. The first stage is a period(hereinafter, referred to as a large-move period) in which the dataprocessor 10 is moved toward the target character string on a largescale as shown in FIG. 9(a). In the large move period, the image isblurred because of the large move of the data processor 10. Therefore,even if a character candidate detection process is performed, nocharacter candidate is detected, as shown in FIG. 9(a). The second stageis a period (hereinafter, referred to as a fine-adjustment period) inwhich the speed of the data processor 10 which was moved in a largescale is reduced to set the target character string within the imagingrange, as shown in FIG. 9(b). In the first half of the fine-adjustmentperiod, the data processor 10 has just started slowing down. Thus, theimage is blurred. A character candidate may be detected or may not bedetected depending on the case. In the second half of thefine-adjustment period, the speed of the data processor 10 issufficiently reduced. Thus, the image is less blurred. The detection ofa character candidate is started, as shown in FIG. 9(b). The third stageis the time (hereinafter, referred to as a framing completion time) whenthe target character string is completely set within the imaging range,as shown in FIG. 9(c). The user can obtain a desired result ideallyimmediately after the framing completion time.

In consideration of the above description, FIG. 8(a) and FIG. 8(b) areexplained. These figures assume a state in which the data processingmethod ideally functions. The result desired by the user can be safelyobtained from the image which is obtained when the framing is completed.

FIG. 8(a) is a pattern diagram showing the relationship between theprocess executed until the acquisition of a desired result and the timerequired for the process when the conventional data processing method isemployed. In the conventional data processing method, a charactercandidate detection process is executed every time an image is obtained.As described above, the image is blurred in the whole large-move periodand the first half of the fine-adjustment period. Therefore, even if acharacter candidate detection process is performed, no charactercandidate is detected (or a character candidate is difficult to bedetected). Thus, the obtained image is preview-displayed, as it is. Inthe whole large-move period and, the first half of the fine-adjustmentperiod, the refresh rate (in other words, the time required until there-execution of a similar process) is equivalent to the sum of the timesrequired for a process for obtaining an image, a character candidatedetection process and a preview display process. In the second half ofthe fine-adjustment period, as described above, the image is lessblurred. Thus, the detection of a character candidate is started. In thesecond half of the fine-adjustment period, the refresh rate isequivalent to the sum of the times required for a process for obtainingan image, a character candidate detection process, a character rowdetection process, a recognition (OCR)/translation process and a previewdisplay process. Thus, the time required to obtain a desired result isconsidered to be time T₁, as shown in FIG. 8(a).

FIG. 8(b) is a pattern diagram showing the relationship between theprocess executed until the acquisition of a desired result and the timerequired, for the process when only the mechanism configured to outputthe first trigger is provided. In this case, at least in the wholelarge-move period, the image is largely blurred; in other words, thedegradation evaluation value is always greater than a predeterminedthreshold. Thus, the first trigger for performing a character candidatedetection process is not output. In the whole large-move period, therefresh rate is equivalent to the sum of the times required only for aprocess for obtaining an image and a preview display process. As shownin FIG. 8(b), the time required to obtain a desired result can belargely reduced in comparison with FIG. 8(a). Specifically, time T₁required to obtain a desired result in the conventional method can bereduced to time T₂, as shown in FIG. 8(b).

Now, this specification explains various icons for suggesting theframing state with reference to FIG. 10. As explained in FIG. 8, threeperiods are present from the start of a framing period to the completionof the framing period. Specifically, the three periods are (1) alarge-move period, (2) a fine-adjustment period and (3) a framingcompletion time. When the user is notified of the difference in thethree periods through preview display by the output module 107, the usercan accurately recognize the framing state. Thereby, framing can beperformed more accurately.

FIG. 10(a) is a pattern diagram showing an example of an icon forsuggesting to the user that the current period is the large-move period(1). When the current period, is equivalent to, of the three periods (1)to (3), the large-move period (1) in FIG. 8, icon 602 for suggestingthat the current period, is the large-move period is displayed on anicon display area 601 on the display of the data processor 10. In FIG.10(a), the arrow icon 602 indicating that the move of the user of thedata processor 10, in other words, the move of the data processor 10 islarge is displayed as the icon for suggesting that the current period isthe large-move period. However, the design of the icon for suggestingthe large-move period is not limited to this example. For example, thecharacter string “large-move period” may be merely displayed on thedisplay. Note that the icon is preferably designed such that the usercan easily recognize that the current period is the large-move period.

FIG. 10(b) is a pattern diagram showing an example of an icon forsuggesting to the user that the current period is the fine-adjustmentperiod (2). When the current period is equivalent to, of the threeperiods (1) to (3), the fine-adjustment period (2) in FIG. 8, icon 603which suggests that the current period is the fine-adjustment period isdisplayed in the icon display area 601 on the display of the dataprocessor 10. In FIG. 10(b), icon 603 which indicates that the target isentering the imaging range of the data processor 10 is displayed as theicon for suggesting that the current period is the fine-adjustmentperiod. However, the design of the icon for suggesting thefine-adjustment period is not limited to this example. For example, thecharacter string “fine-adjustment period” may be merely displayed on thedisplay. Note that the icon is preferably designed such that the usercan easily recognize that the current period is the fine-adjustmentperiod.

FIG. 10(c) is a pattern diagram showing an example of an icon forsuggesting to the user that the current period is the framing completiontime (3). When the current period is equivalent to, of the three periods(1) to (3), the framing completion time (3) in FIG. 8, icon 604 whichsuggests that the current period is the framing completion time isdisplayed in the icon display area 601 on the display of the dataprocessor 10. In FIG. 10(c), icon 604 which indicates that the targetfinished entering the imaging range of the data processor 10 (in otherwords, the target is set within the imaging range) is displayed as theicon for suggesting the framing completion time. However, the design ofthe icon for suggesting the framing completion time is not limited tothe above example. For example, the character string “framing completiontime” may be merely displayed on the display. Note that the icon ispreferably designed such that the user can easily recognize that thecurrent period is the framing completion time.

In FIG. 10, the icon suggesting each of the three periods (1) to (3) isdisplayed. However, for example, the sound corresponding to each of thethree periods may be output by the output module 107.

In addition to the suggestion of the three periods (1) to (3), it ispossible to suggest the degradation evaluation value to the user, usinga graph superimposed on the preview display by the output module 107.Further, it is possible to suggest the position of a character candidatedetected by the character candidate detector 104 or a character rowdetected by the character row detector 105 to the user, using a frame,etc. With reference to FIG. 11, this specification explains a graph iconfor suggesting the degradation evaluation value.

FIG. 11 is a pattern diagram showing an example of a graph icon forsuggesting the degradation evaluation value. The graph (here, the bargraph) showing the degradation evaluation value is displayed in a graphdisplay area 701 on the display of the data processor 10. FIG. 11 showsa graph 702 indicating the degradation evaluation value calculated bythe degradation evaluation module 102. FIG. 11 also shows a line 703indicating a predetermined, threshold in the degradation evaluationmodule 102. According to FIG. 11, the user can visually recognize thatthe degradation evaluation value calculated by the degradationevaluation module 102 is less than the predetermined threshold (in otherwords, the first trigger is output). When the degradation evaluationvalue is less than the predetermined threshold (in other words, thefirst trigger is output), the user can more easily recognize that thefirst, trigger is output by differentiating the color or brightness ofthe graph from that of the graph which is displayed when the degradationevaluation value exceeds the threshold.

As shown in FIG. 11, the user is notified of the degradation evaluationvalue. This notification enables the user to more specifically predict,when a desired result is not obtained from detection, recognition ortranslation of a character row, the cause of this failure; for example,the user can predict that the cause is the large-move period, or thefailure of detection of a character candidate because the targetcharacter is too far or the inclination of the character is too steep.

In the above description, the degradation evaluation module 102 does notperform the determination process of step S2 in FIG. 7 after the startof the process subsequent to the character candidate detection (steps S3to S7 in FIG. 7). However, in consideration of the possibility that theuser restarts the framing along the way, the degradation evaluationmodule 102 may continue the determination process of step S2 in FIG. 7as background even after the process subsequent to the charactercandidate detection (steps S3 to S7 in FIG. 7) is begun. In, this case,when the degradation evaluation value exceeds a threshold, thedegradation evaluation module 102 may immediately stop outputting thefirst trigger, interrupt the process subsequent to the charactercandidate detection (steps S3 to S7) and proceed to step S8 forpreview-displaying the obtained image as it is. By this configuration,when the user starts moving the data processor again to restart theframing, the refresh rate for preview display can be increased again inassociation with the move.

With reference to FIG. 12, this specification explains an example of thehardware configuration of the data processor 10.

FIG. 12 shows the hardware configuration of the data processor. The dataprocessor 10 of FIG. 12 includes a CPU 801, a RAM 802, a ROM 803, an HDD804, a LAN 805, an input device 806, a display 807, an externalinterface 808, an external storage device 809, a camera 810 and anacceleration sensor 811.

The CPU 801 is a processor configured to control the components of thedata processor 10. The CPU 801 executes a character row detectionprogram loaded from the HDD 804 into the RAM 802. The CPU 801 is capableof functioning as a processor configured to perform the above dataprocess by executing the character row detection program. The CPU 801 isalso capable of loading a character row detection program from theexternal storage device 809 (for example, a USB device) into the RAM 802and executing the program. In addition to the character row detectionprogram, for example, an image used to perform a data process can beloaded from the external storage device 809.

The input device 806 is a keyboard, a mouse, a touchpanel, or anothertype of input device. The display 807 is a device configured to displaythe result of various processes performed by the data processor 10. Thecamera 810 is a device configured to capture an image that can be thetarget for a data process. The acceleration sensor 811 is a deviceconfigured to obtain the degradation evaluation value.

In the above first embodiment, only when the possibility that acharacter candidate is detected is high as a result of determination,the degradation evaluation module 102 outputs the first trigger forperforming a character candidate detection process. Because of thisdegradation module 102, it is possible to maintain a high refresh rateand shorten the time required for framing as explained in FIG. 8. Inthis manner, the user can easily perform framing.

Second Embodiment

This specification explains a second embodiment with reference to FIG.13. In the present embodiment, a data processor 10 further includes adensity evaluation module 108 as shown in FIG. 13 in a manner differentfrom that of the first embodiment. In the description below, theexplanation of the same functions or configurations as those of thefirst embodiment is omitted. Functions or configurations different fromthose of the first embodiment are mainly explained below.

The density evaluation module 108 is a function module provided so as tostand between a character candidate detector 104 and a character rowdetector 105. After receiving the input of first detection resultinformation output by the character candidate detector 104, the densityevaluation module 108 performs a density evaluation process as explainedlater.

In general, characters are densely described so as to be aligned in onedirection (for example, a lateral or vertical direction). Therefore,when character candidates are detected sparsely in the image by thecharacter candidate detector 104, the possibility that a character row(character string) is detected from the image is low. When charactercandidates are detected densely in the image by the character candidatedetector 104, the possibility that a character row (character string) isdetected from the image is high.

The density evaluation module 108 performs a density evaluation process,using the above features. Specifically, the density evaluation module108 calculates the character candidate area (hereinafter, referred to asthe density evaluation value) per unit area by diving the sum of theareas occupied by (or the numbers of pixels of) a predetermined numberof character candidates or more detected by the character candidatedetector 104 by the area (the number of pixels) of the whole image.Based on the calculated density evaluation value, the density evaluationmodule 108 performs a density evaluation process for determining whetherthe possibility of detection of a character row is high or low. When thepossibility of detection of a character row is high as a result ofdetermination, the density evaluation module 108 outputs a secondtrigger for performing a character row detection process to thecharacter row detector 105. When the possibility of detection of acharacter row is low as a result of determination, the densityevaluation module 108 outputs a command for performing a preview displayprocess to an output module 107.

Referring to FIG. 14, this specification more specifically explains therelationship between the distribution of character candidates detectedby the character candidate detector 104 and the density evaluation valuecalculated by the density evaluation module 108. Here, even if theposition of the actual characters is slightly different from theposition of the detection window, the character candidate detector 104is assumed to detect the detection window as a character candidate (inother words, to detect the area in which the detection window is locatedas an area in which a character is considered to be present). Thecharacter candidate detector 104 is assumed to detect each of detectionwindows for one character as a character candidate in a portion in whichcharacters are densely described.

In FIG. 14, the number “901” exemplarily shows a case in which acharacter candidate is incorrectly detected from an image having nocharacter. This case shows that the distribution of character candidatesis discrete. In FIG. 14, the number “905” shows a diagram correspondingto the number “901”. According to this diagram, the area of the wholeimage is forty times that of a detection window 904. Thus, the densityevaluation value can be obtained as 0.05 (=2/40) by the abovecalculation method.

In FIG. 14, the number “902” exemplarily shows a case in which thecharacter interval is wide; in other words, a plurality of charactercandidates are detected from the image in which the character row“ABCDEF” is sparsely described. This case shows that a plurality of(here, eight) character candidates are detected with a slightly highdensity around the character row “ABCDEF”. In FIG. 14, the number “906”shows a diagram corresponding to the number “902”. According to thisdiagram, the density evaluation value can be obtained as 0.2 (=8/40).

In FIG. 14, the number “903” exemplarily shows a case in which thecharacter interval is narrow; in other words, a plurality of charactercandidates are detected from the image in which the character row“ABCDEFG” is densely described. This case shows that a plurality of(here, sixteen) character candidates are densely detected around thecharacter row “ABCDEFG”. In FIG. 14, the number “907” shows a diagramcorresponding to the number “903”. According to this diagram, thedensity evaluation value can be obtained as 0.4 (=16/40).

The relationship between the distribution of character candidates andthe density evaluation value can be considered as follows. The moresparsely character candidates are distributed, the less the densityevaluation value is. The more densely character candidates aredistributed, the greater the density evaluation value is. In otherwords, the more sparsely character candidates are distributed, the morelikely the density evaluation module 108 outputs a command forperforming a preview display process to the output module 107. The moredensely character candidates are distributed, the more likely thedensity evaluation module 108 outputs the second trigger for performinga character row detection process to the character row detector 105.

The sum of the areas of character candidates is increased by the area inwhich the candidates overlap with each other in comparison with the areawhich is actually covered by the candidates. As characters are describedmore densely, the sum of the areas of character candidates tends to beincreased. This is because each of detection windows for one characteris detected as a character candidate.

In place of the above density evaluation value, for example, it ispossible to use, from the densities of character candidates in smallareas of the image, the maximum value in the whole image as the densityevaluation value. In this case, the density of character candidates iscalculated for each small area. By obtaining the maximum value of thedensities, it is possible to convert the situation in which charactercandidates are locally distributed at high density in the image intonumbers.

When the size of the target character is limited to a narrow range, andthe number of images of a resolution pyramid image 204 can be keptsmall, the difference in the size of detection windows may be ignored.Therefore, the ratio of the number of character candidates to apredetermined value (for example, a value obtained by dividing theaverage detection window area by the image area) may be obtained toobtain an approximate value of the density of character candidates.

Now, this specification explains an example of the operation of the dataprocessor 10 according to the second embodiment, referring to theflowchart of FIG. 15.

First, an image acquisition module 101 obtains an image captured by acamera function (step S11). Subsequently, a degradation evaluationmodule 102 obtains, from an acceleration sensor incorporated, into thedata processor 10, etc., the degradation evaluation value at the time ofcapturing, the image obtained by the image acquisition module 101. Thedegradation evaluation module 102 determines whether or not the obtaineddegradation evaluation value is less than or equal to a predeterminedthreshold (step S12). When the degradation evaluation value exceeds thethreshold as a result of the determination process in step S12 (NO instep S12), the process proceeds to step S19 for preview-displaying theobtained image as it is as described later.

When the degradation evaluation value is less than or equal to thethreshold as a result of the determination process in step S12 (YES instep S12), the degradation evaluation module 102 outputs a first triggerfor performing a character candidate detection process to the charactercandidate detector 104. After receiving the input of the first triggeroutput by the degradation evaluation module 102, the character candidatedetector 104 applies a character candidate detection process to theimage obtained by the image acquisition module 101 (step S13).

Subsequently, the character candidate detector 104 determines whether ornot a predetermined number of character candidates or more are detectedas a result of the character candidate detection process in step S13(step S14). When a predetermined number of character candidates or moreare not detected as a result of the determination, process in step S14(NO in step S14), the process proceeds to step S19 forpreview-displaying the obtained image as it is as described later.

When a predetermined number of character candidates or more are detectedas a result of the determination process in step S14 (YES in step S14),the density evaluation module 108 calculates the density evaluationvalue based on the first detection result information obtained as aresult of the character candidate detection process in step S13, anddetermines whether or not the calculated density evaluation value isgreater than or equal to a predetermined threshold (step S15). When thedensity evaluation value is less than the threshold as a result of thedetermination process in, step S15 (NO in step S15), the processproceeds to step S19 for preview-displaying the obtained image as it isas described later.

When the density evaluation value is greater than or equal to thethreshold as a result of the determination process in step S15 (YES instep S15), the density evaluation module 108 outputs the second triggerfor performing a character row detection process to the character rowdetector 105. After receiving the input of the second trigger output bythe density evaluation module 108, the character row detector 105applies a character row detection process to the image obtained by theimage acquisition module 101 (step S16).

Subsequently, the character row detector 105 determines whether or notone or more character rows are detected as a result of the character rowdetection process in step S16 (step S17). When no character row isdetected as a result of the determination process in step S17 (NO instep S17), the process proceeds to step S19 for preview-displaying theobtained image as it is as described later.

When one or more character rows are detected as a result of thedetermination process in step S17 (YES in step S17), the character rowdetector 105 outputs second detection result information obtained as aresult of the character row detection process in step S16 to anapplication module 106. The application module, 106 performs a process(for example, a character recognition process or a translation process)unique to an application installed in advance based on the seconddetection result information output by the character row detector 105,and outputs the process result information showing the process result tothe output module 107 (step S18).

After receiving the input of the process result information output bythe application module 106, the output module 107 performs a previewdisplay process for superimposing the process result information on theimage obtained by the image acquisition module 101 and displaying theimage on the display. When the input of a command for performing apreview display process is received from each module different from theapplication module 106, the output module 107 at least displays theimage obtained by the image acquisition module 101 on the display as itis (step S19) and terminates the process.

Now, this specification explains differences between the conventionaldata processing method, the data processing method of the firstembodiment and the data processing method of the present embodiment,referring to FIG. 16. Mainly, this specification explains differences interms of a framing period and a refresh rate.

FIG. 16(a) and FIG. 16(b) are the same as FIG. 8(a) and FIG. 8(b)described above, respectively. Thus, the detailed explanation of thefigures is omitted.

FIG. 16(c) is a pattern diagram showing the relationship between theprocess executed until the acquisition of a desired result and the timerequired for the process when the mechanism configured to output thefirst trigger and the mechanism configured to output the second triggerare provided. In this case, the first trigger for performing a charactercandidate detection process is not output at least in the wholelarge-move period in a matter similar to that of FIG. 16(b). Thus, it ispossible to maintain a high refresh rate in the whole large-move periodin comparison with the conventional method. In FIG. 16(a) and FIG.16(b), a character row detection process is performed at least in thesecond half of the fine-adjustment period even if character candidatessparsely arranged are detected by a character candidate detectionprocess. Thus, the refresh rate is low. However, in FIG. 16(c), themechanism configured to output the second trigger is provided.Therefore, even if character candidates sparsely arranged, are detectedby a character candidate detection process, the second trigger is notoutput; in other words, a character row detection, process is notperformed. In, this manner, the refresh rate can, be increased by thisamount in comparison with FIG. 16(a) and FIG. 16(b). The time requiredto obtain a desired result can be reduced to time T₃, as shown in FIG.16(c).

With reference to FIG. 17, this specification explains a graph icon forsuggesting the degradation evaluation value.

FIG. 17 is a pattern diagram showing an example of a graph icon forsuggesting the density evaluation value. The graph (here, the bar graph)showing the density evaluation value is displayed in a graph displayarea 1001 on the display of the data processor 10 by the output module107. Here, the graph display area 701 shown in FIG. 11 explained aboveand, the graph display area 1001 shown in FIG. 17 are assumed to beprovided in different positions. In this manner, the user can visuallyrecognize both the degradation evaluation value and the densityevaluation value. FIG. 17 shows a graph 1002 indicating the densityevaluation value calculated by the density evaluation module 108. FIG.17 also shows a line 1003 indicating a predetermined threshold in thedensity evaluation module 108. According to FIG. 17, the user canvisually recognize that the density evaluation value calculated by thedensity evaluation module 108 is greater than the predeterminedthreshold (in other words, the second trigger is output). When thedensity evaluation value is greater than the predetermined threshold (inother words, the second trigger is output), the user can more easilyrecognize that the second trigger is output by differentiating the coloror brightness of the graph from that of the graph which is displayedwhen the density evaluation value is less than the threshold.

As shown in FIG. 17, in addition to the degradation evaluation value,the user is notified of the density evaluation value. This notificationenables the user to more specifically predict, when a desired result isnot obtained from detection, recognition or translation of a characterrow, the cause of this failure. For example, the user can predict thatthe cause is the large-move period, or the failure of detection of acharacter candidate because the target character is too far or theinclination of the character is too steep. Further, the user can predictthat the cause is the insufficient density of the detected charactercandidates, or the failure of detection of a character row even if thedensity of the detected character candidates is sufficient.

In the above second embodiment, the density evaluation module 108 isfurther provided. The density evaluation module 108 outputs the secondtrigger for performing a character row detection process only when thepossibility that a character row is detected is high as a result ofdetermination. Therefore, it is possible to maintain a high refresh rateand further shorten the time required for framing as explained in FIG.16 in comparison with the first embodiment.

Third Embodiment

This specification explains a third embodiment with reference to theflowchart of FIG. 18. In the present embodiment, in a manner differentfrom that of the first or second embodiment, a character candidatedetector 104 does not perform a character candidate detection processimmediately after receiving the input of a first trigger. Instead, thecharacter candidate detector 104 performs a character candidatedetection process if the input of the first trigger output by adegradation evaluation module 102 is still received after the elapse ofa certain period (for example, approximately 0.5 seconds) from thereceipt of the input of the first trigger.

As described above, a character candidate detection process is performedif the input of the first trigger has been continuously received for acertain period (in other words, a character candidate detection processis performed with the introduction of a delay frame). In this manner,even if an action of revoking the input of the first trigger isperformed (for example, a data processor 10 is moved in a large scale)immediately after the first trigger is output by the degradationevaluation module 102, a character candidate detection process is notperformed redundantly.

This specification explains an example of the operation of the dataprocessor 10 according to the third embodiment, referring to theflowchart of FIG. 18.

First, an image acquisition module 101 obtains an Image captured by acamera function (step S21). Subsequently, the degradation, evaluationmodule 102 obtains, from an acceleration sensor incorporated into thedata processor 10, the degradation evaluation value at the time ofcapturing the image obtained by the image acquisition module 101. Thedegradation evaluation module 102 determines whether or not the obtaineddegradation evaluation value is less than or equal to a predeterminedthreshold (step S22). When, the degradation, evaluation value exceedsthe threshold as a result of the determination process in step S22 (NOin step S22), the process proceeds to step S30 for preview-displayingthe obtained image as it is as described later.

When the degradation evaluation value is less than or equal to thethreshold as a result of the determination process in step S22 (YES instep S22), the degradation evaluation module 102 outputs the firsttrigger for performing a character candidate detection process to thecharacter candidate detector 104. After receiving the input of the firsttrigger output by the degradation evaluation module 102, the charactercandidate detector 104 determines whether or not the input of the firsttrigger has been continuously received for a certain period (step S23).If the input of the first trigger has not been continuously received fora certain period as a result of the determination process in step S23(NO in step S23), the process proceeds to step S30 forpreview-displaying the obtained image as it is as described later.

If the input of the first trigger output by the degradation evaluationmodule 102 has been continuously received for a certain period as aresult of the determination process in step S23 (YES in step S23), thecharacter candidate detector 104 applies a character candidate detectionprocess to the image obtained by the image acquisition module 101 (stepS24).

Subsequently, the character candidate detector 104 determines whether ornot a predetermined number of character candidates or more are detectedas a result of the character candidate detection process in step S24(step S25). When a predetermined number of character candidates or moreare not detected as a result of the determination process in step S25(NO in step S25), the process proceeds to step S30 forpreview-displaying the obtained image as it is as described later.

When a predetermined number of character candidates or more are detectedas a result of the determination process in step S25 (YES in step S25),a density evaluation module 108 calculates the density evaluation valuebased on first detection result information obtained as a result of thecharacter candidate detection, process in step S24, and determineswhether or not the calculated density evaluation value is greater thanor equal to a predetermined threshold (step S26). When the densityevaluation value is less than the threshold as a result of thedetermination process in step S26 (NO in step S26), the process proceedsto step S30 for preview-displaying the obtained image as it is asdescribed later.

When the density evaluation value is greater than or equal to thethreshold as a result of the determination process in step S26 (YES instep S26), the density evaluation module 108 outputs a second triggerfor performing a character row detection process to a character rowdetector 105. After receiving the input of the second trigger output bythe density evaluation module 108, the character row detector 105applies a character row detection process to the image obtained by theimage acquisition module 101 (step S27).

Subsequently, the character row detector 105 determines whether or notone or more character rows are detected as a result of the character rowdetection process in step S27 (step S28). When no character row isdetected as a result of the determination process in step S28 (NO instep S28), the process proceeds to step S30 for preview-displaying theobtained image as it is as described later.

When one or more character rows are detected as a result of thedetermination process in step S28 (YES in step S28), the character rowdetector 105 outputs second detection result information obtained as aresult of the character row detection process in step S27 to anapplication module 106. The application module 106 performs a process(for example, a character recognition process or a translation process)unique to an application installed in advance based on the seconddetection result information output by the character row detector 105,and outputs the process result information showing the process result toan output module 107 (step S29).

After receiving the input of the process result information output bythe application module 106, the output module 107 performs a previewdisplay process for superimposing the process result information on theimage obtained by the image acquisition module 101 and displaying theimage on the display. When the input of a command for performing apreview display process is received from each module different from theapplication module 106, the output module 107 at least displays theimage obtained by the image acquisition module 101 on the display as itis (step S30) and terminates the process.

Now, this specification explains differences between the conventionaldata processing method, the data processing method of the firstembodiment, the data processing method of the second embodiment and thedata processing method of the present embodiment. Mainly, thisspecification explains differences in terms of a framing period and arefresh rate.

FIG. 19(a) to FIG. 19(c) are the same as FIG. 16(a) to FIG. 16(c)described above, respectively. Thus, the detailed explanation of thefigures is omitted.

FIG. 19(d) is a pattern diagram showing the relationship between theprocess executed until the acquisition of a desired result and the timerequired for the process when the mechanism configured to output thefirst trigger, the mechanism configured to output the second trigger andthe delay frame are provided. In this case, it is possible to maintain ahigh refresh rate in the whole large-move period and the second half ofthe fine-adjustment period in a manner similar to that of FIG. 19(c). InFIG. 19(b) and FIG. 19(c), a character candidate detection process isperformed immediately after the output of the first trigger at least inthe first half of the fine-adjustment period. Therefore, if an action ofrevoking the output of the first trigger is made, this action cannot beappropriately dealt with. However, in FIG. 19(d), a delay frame isintroduced, and thus, a character candidate detection process is notperformed when an action of revoking the output of the first trigger ismade. In this manner, the refresh rate can be kept high by this amountin comparison with FIG. 19(b) and FIG. 19(c). The time required toobtain a desired result can be reduced to time T₄, as shown in FIG.19(d).

In the above third embodiment, the delay frame is introduced into thecharacter candidate detector 104 to deal with an action of revoking theinput of the first trigger. Thus, it is possible to maintain a highrefresh rate and further shorten the time required for framing asexplained in FIG. 19 in comparison with the first and, secondembodiments.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the inventions. Indeed, the novel embodiments described hereinmay be embodied in a variety of other forms; furthermore, variousomissions, substitutions and changes in the form of the embodimentsdescribed herein may be made without departing from the spirit of theinventions. The accompanying claims and their equivalents are intendedto cover such forms or modifications as would fall within the scope andspirit of the inventions.

What is claimed is:
 1. A data processor comprising: an image acquisitionmodule configured to acquire an image obtained by capturing a charactersurface on which a character row including a plurality of characters isdescribed; a degradation evaluation module configured to evaluatewhether a possibility that an image area which seems to correspond to acharacter is detected from the image is high based on a degradationevaluation value indicating a degree of degradation of the image; afirst output module configured to output a first trigger for performinga process for detecting the image area when the possibility is high as aresult of evaluation by the degradation evaluation module, the firstoutput module configured to output a command for displaying the image asit is on a display when the possibility is low as a result of theevaluation; and a display module configured to display, by detecting apredetermined number of image areas or more which seem to correspond tocharacters from the image, an image prepared by processing the imageareas in accordance with the output first trigger, or display the imageacquired by the image acquisition module as it is in accordance with theoutput command.
 2. The data processor of claim 1, further comprising: adensity evaluation module configured to evaluate whether a possibilitythat a character row is detected from the acquired image is high basedon a density evaluation value indicating to what extent the detectedpredetermined number of image areas or more occupy a total area of theacquired image; and a second output module configured to output a secondtrigger for performing a process for detecting the character row whenthe possibility is high as a result of evaluation by the densityevaluation module, the second output module configured to output thecommand when the possibility is low as a result of the evaluation by thedensity evaluation module, wherein the display module displays an imageprepared by processing the character row by detecting the character rowfrom the detected predetermined number of image areas or more inaccordance with the output second trigger.
 3. The data processor ofclaim 1, wherein the degradation evaluation module evaluates whether thedegradation evaluation value is less than or equal to a predeterminedthreshold, using a pose change amount which is measured at the time ofcapturing the acquired image as the degradation evaluation value, andthe first output module outputs the first trigger when the degradationevaluation value is less than or equal to the threshold as a result ofevaluation by the degradation evaluation module.
 4. The data processorof claim 3, wherein the first output module outputs the first triggerwhen the degradation evaluation value is less than or equal to thethreshold as a result of the evaluation by the degradation evaluationmodule, and further, when an imaging unit configured to capture theacquired image comes into focus.
 5. The data processor of claim 1,wherein the image areas which seem to correspond to characters aredetected from the acquired image when input of the output first triggerhas been continuously received for a certain period.
 6. A dataprocessing method comprising: acquiring an image obtained by capturing acharacter surface on which a character row including a plurality ofcharacters is described; evaluating whether a possibility that an imagearea which seems to correspond to a character is detected from the imageis high based on a degradation evaluation value indicating a degree ofdegradation of the image; outputting a first trigger for performing aprocess for detecting the image area when the possibility is high as aresult of evaluation, and outputting a command for displaying the imageas it is on a display when the possibility is low as a result of theevaluation; and displaying, by detecting a predetermined number of imageareas or more which seem to correspond to characters from the acquiredimage, an image prepared by processing the image areas in accordancewith the first trigger, or displaying the acquired image as it is inaccordance with the output command.
 7. The data processing method ofclaim 6, further comprising: evaluating whether a possibility that acharacter row is detected from the acquired image is high based on adensity evaluation value indicating to what extent the detectedpredetermined number of image areas or more occupy a total area of theacquired image; and outputting a second trigger for performing a processfor detecting the character row when the possibility is high as a resultof evaluation, and outputting the command when the possibility is low asa result of the evaluation, wherein the displaying includes displayingan image prepared by processing the character row by detecting thecharacter row from the detected predetermined number of image areas ormore in accordance with the output second trigger.
 8. The dataprocessing method of claim 6, wherein the evaluating includes evaluatingwhether the degradation evaluation value is less than or equal to apredetermined threshold, using a pose change amount which is measured atthe time of capturing the acquired image as the degradation evaluationvalue, and the outputting includes outputting the first trigger when thedegradation evaluation value is less than or equal to the threshold as aresult of evaluation.
 9. The data processing method of claim 8, whereinthe outputting includes outputting the first trigger when thedegradation evaluation value is less than or equal to the threshold, andfurther, when an imaging unit configured to capture the acquired imagecomes into focus.
 10. The data processing method of claim 6, wherein theimage areas which seems to correspond to characters are detected fromthe acquired image when input of the output first trigger has beencontinuously received for a certain period.
 11. A non-transitorycomputer-readable storage medium storing instructions executed by acomputer, wherein the instructions, when executed by the computer, causethe computer to perform: acquiring an image obtained by capturing acharacter surface on which a character row including a plurality ofcharacters is described; evaluating whether a possibility that an imagearea which seems to correspond to a character is detected from the imageis high based on a degradation evaluation value indicating a degree ofdegradation of the image; outputting a first trigger for performing aprocess for detecting the image area when the possibility is high as aresult of evaluation, and outputting a command for displaying the imageas it is on a display when the possibility is low as a result of theevaluation; and displaying, by detecting a predetermined number of imageareas or more which seem to correspond to characters from the acquiredimage, an image prepared by processing the image areas in accordancewith the first trigger, or displaying the acquired image as it is inaccordance with the output command.
 12. The storage medium of claim 11,further cause the computer to perform: evaluating whether a possibilitythat a character row is detected from the acquired image is high basedon a density evaluation value indicating to what extent the detectedpredetermined number of image areas or more occupy a total area of theacquired image; and outputting a second trigger for performing a processfor detecting the character row when the possibility is high as a resultof evaluation, and outputting the command when the possibility is low asa result of the evaluation, wherein the displaying includes displayingan image prepared by processing the character row by detecting thecharacter row from the detected predetermined number of image areas ormore in accordance with the output second trigger.
 13. The storagemedium of claim 11, wherein the evaluating includes evaluating whetherthe degradation evaluation value is less than or equal to apredetermined threshold, using a pose change amount which is measured atthe time of capturing the acquired image as the degradation evaluationvalue, and the outputting includes outputting the first trigger when thedegradation evaluation value is less than or equal to the threshold as aresult of evaluation.
 14. The storage medium of claim 13, wherein theoutputting includes outputting the first trigger when the degradationevaluation value is less than or equal to the threshold, and further,when an imaging unit configured to capture the acquired image comes intofocus.
 15. The storage medium of claim 11, wherein the image areas whichseems to correspond to characters are detected from the acquired imagewhen input of the output first trigger has been continuously receivedfor a certain period.